Model-based Clustering of Count Processes

Tin Lok James Ng () and Thomas Brendan Murphy ()
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Tin Lok James Ng: University of Wollongong
Thomas Brendan Murphy: University College Dublin

Journal of Classification, 2021, vol. 38, issue 2, No 2, 188-211

Abstract: Abstract A model-based clustering method based on Gaussian Cox process is proposed to address the problem of clustering of count process data. The model allows for nonparametric estimation of intensity functions of Poisson processes, while simultaneous clustering count process observations. A logistic Gaussian process transformation is imposed on the intensity functions to enforce smoothness. Maximum likelihood parameter estimation is carried out via the EM algorithm, while model selection is addressed using a cross-validated likelihood approach. The proposed model and methodology are applied to two datasets.

Keywords: Count process; Clustering; Gaussian process; Gaussian Cox process; Mixture models (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s00357-020-09363-4

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